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| Adaptive Maximum Variation Sampling× | Adaptive Stratified Sampling× | |
|---|---|---|
| Fachgebiet | Umfragemethodik | Umfragemethodik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1990s–2000s (practice codified in qualitative methods literature) | 1990s (formal development from Thompson 1990 onward) |
| Urheber≠ | Synthesizes Patton (maximum variation) and Thompson (adaptive sampling) | Steven K. Thompson (adaptive sampling); allocation adaptations by Salehi, Seber, and others |
| Typ≠ | Adaptive purposive qualitative sampling strategy | Probability-based adaptive sampling design |
| Wegweisende Quelle≠ | Patton, M. Q. (1990). Qualitative Evaluation and Research Methods (2nd ed.). Sage. [Maximum variation sampling, pp. 169–183] ISBN: 978-0803937796 | Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059. DOI ↗ |
| Aliasnamen | adaptive purposive maximum variation sampling, iterative maximum variation sampling, adaptive heterogeneous sampling, AMVS | ASS, adaptive stratified design, stratified adaptive sampling, adaptive allocation stratified sampling |
| Verwandt≠ | 5 | 6 |
| Zusammenfassung≠ | Adaptive maximum variation sampling is a purposive qualitative sampling strategy that combines the logic of maximum variation sampling — deliberately selecting cases that differ as widely as possible on key dimensions — with an adaptive, iterative recruitment process. Rather than fixing the full sample in advance, the researcher continuously reviews emerging data to identify which types of cases are underrepresented and recruits new participants to fill those gaps, maximizing heterogeneity throughout data collection. | Adaptive stratified sampling divides the population into strata and then applies an adaptive rule within each stratum: whenever an initially selected unit satisfies a pre-specified condition (e.g., a rare species is found, a variable exceeds a threshold), neighboring or related units are added to the sample. This combines the variance-reduction power of stratification with the ability to concentrate sampling effort where the phenomenon of interest is actually present. |
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